Biomedical applications of micro or nanorobots require active movement through complex biological fluids. These are generally non-Newtonian (viscoelastic) fluids that are characterized by complicated networks of macromolecules that have size-dependent rheological properties. It has been suggested that an untethered microrobot could assist in retinal surgical procedures. To do this it must navigate the vitreous humor, a hydrated double network of collagen fibrils and high molecular-weight, polyanionic hyaluronan macromolecules. Here, we examine the characteristic size that potential robots must have to traverse vitreous relatively unhindered. We have constructed magnetic tweezers that provide a large gradient of up to 320 T/m to pull sub-micron paramagnetic beads through biological fluids. A novel two-step electrical discharge machining (EDM) approach is used to construct the tips of the magnetic tweezers with a resolution of 30 mu m and high aspect ratio of similar to 17:1 that restricts the magnetic field gradient to the plane of observation. We report measurements on porcine vitreous. In agreement with structural data and passive Brownian diffusion studies we find that the unhindered active propulsion through the eye calls for nanorobots with cross-sections of less than 500 nm.

Physicians are collecting an ever increasing amount of data describing the health state of their patients. Is new knowledge about diseases hidden in this data, which could lead to better therapies? The field of Machine Learning in Biomedicine is concerned with the development of approaches which help to gain such insights from massive biomedical data.

Objective: Motor neuroscience and brain-machine interface (BMI) design is based on examining how the brain controls voluntary movement, typically by recording neural activity and behavior from animal models. Recording technologies used with these animal models have traditionally limited the range of behaviors that can be studied, and thus the generality of science and engineering research. We aim to design a freely-moving animal model using neural and behavioral recording technologies that do not constrain movement.
Approach: We have established a freely-moving rhesus monkey model employing technology that transmits neural activity from an intracortical array using a head-mounted device and records behavior through computer vision using markerless motion capture. We demonstrate the excitability and utility of this new monkey model, including the first recordings from motor cortex while rhesus monkeys walk quadrupedally on a treadmill.
Main results: Using this monkey model, we show that multi-unit threshold-crossing neural activity encodes the phase of walking and that the average ring rate of the threshold crossings covaries with the speed of individual steps. On a population level, we find that neural state-space trajectories of walking at different speeds have similar rotational dynamics in some dimensions that evolve at the step rate of walking, yet robustly separate by speed in other state-space dimensions.
Significance: Freely-moving animal models may allow neuroscientists to examine a wider range of behaviors and can provide a flexible experimental paradigm for examining the neural mechanisms that underlie movement generation across behaviors and environments. For BMIs, freely-moving animal models have the potential to aid prosthetic design by examining how neural encoding changes with posture, environment, and other real-world context changes. Understanding this new realm of behavior in more naturalistic settings is essential for overall progress of basic motor neuroscience and for the successful translation of BMIs to people with paralysis.

Biological microorganisms swim with flagella and cilia that execute nonreciprocal motions for low Reynolds number (Re) propulsion in viscous fluids. This symmetry requirement is a consequence of Purcell's scallop theorem, which complicates the actuation scheme needed by microswimmers. However, most biomedically important fluids are non-Newtonian where the scallop theorem no longer holds. It should therefore be possible to realize a microswimmer that moves with reciprocal periodic body-shape changes in non-Newtonian fluids. Here we report a symmetric `micro-scallop', a single-hinge microswimmer that can propel in shear thickening and shear thinning (non-Newtonian) fluids by reciprocal motion at low Re. Excellent agreement between our measurements and both numerical and analytical theoretical predictions indicates that the net propulsion is caused by modulation of the fluid viscosity upon varying the shear rate. This reciprocal swimming mechanism opens new possibilities in designing biomedical microdevices that can propel by a simple actuation scheme in non-Newtonian biological fluids.

The helix has remarkable qualities and is prevalent in many fields including mathematics, physics, chemistry, and biology. This shape, which is chiral by nature, is ubiquitous in biology with perhaps the most famous example being DNA. Other naturally occurring helices are common at the nanoscale in the form of protein secondary structures and in various macromolecules. Nanoscale helices exhibit a wide range of interesting mechanical, optical, and electrical properties which can be intentionally engineered into the structure by choosing the correct morphology and material. As technology advances, these fabrication parameters can be fine-tuned and matched to the application of interest. Herein, we focus on the fabrication and properties of nanohelices grown by a dynamic shadowing growth method combined with fast wafer-scale substrate patterning which has a number of distinct advantages. We review the fabrication methodology and provide several examples that illustrate the generality and utility of nanohelices shadow-grown on nanopatterns.

Living organisms have a very effective method for eliminating cells that are no longer needed: programmed death. Researchers in the group of Ana García Sáez work with a protein called Bax, a key regulator of apoptosis that creates pores with a flexible diameter inside the outer mitochondrial membrane. This step inevitably triggers the final death of the cell. These insights into the role of important key enzymes in setting off apoptosis could provide useful for developing drugs that can directly influence apoptosis.

In Proceedings of the IEEE International Conference on Intelligent Robots and Systems, pages: 2244 - 2249, IROS, 2014, clmc (inproceedings)

Abstract

Model-based control is essential for compliant controland force control in many modern complex robots, like humanoidor disaster robots. Due to many unknown and hard tomodel nonlinearities, analytical models of such robots are oftenonly very rough approximations. However, modern optimizationcontrollers frequently depend on reasonably accurate models,and degrade greatly in robustness and performance if modelerrors are too large. For a long time, machine learning hasbeen expected to provide automatic empirical model synthesis,yet so far, research has only generated feasibility studies butno learning algorithms that run reliably on complex robots.In this paper, we combine two promising worlds of regressiontechniques to generate a more powerful regression learningsystem. On the one hand, locally weighted regression techniquesare computationally efficient, but hard to tune due to avariety of data dependent meta-parameters. On the other hand,Bayesian regression has rather automatic and robust methods toset learning parameters, but becomes quickly computationallyinfeasible for big and high-dimensional data sets. By reducingthe complexity of Bayesian regression in the spirit of local modellearning through variational approximations, we arrive at anovel algorithm that is computationally efficient and easy toinitialize for robust learning. Evaluations on several datasetsdemonstrate very good learning performance and the potentialfor a general regression learning tool for robotics.

What is intelligence, and can we create it? Animals can perceive, reason, react and learn, but they are just one example of an intelligent system. Intelligent systems could be robots as large as humans, helping with search-and- rescue operations in dangerous places, or smart devices as tiny as a cell, delivering drugs to a target within the body. Even computing systems can be intelligent, by perceiving the world, crawling the web and processing â??big dataâ?? to extract and learn from complex information.Understanding not only how intelligence can be reproduced, but also how to build systems that put these ideas into practice, will be a challenge. Small intelligent systems will require new materials and fabrication methods, as well as com- pact information processors and power sources. And for nano-sized systems, the rules change altogether. The laws of physics operate very differently at tiny scales: for a nanorobot, swimming through water is like struggling through treacle.Researchers at the Max Planck Institute for Intelligent Systems have begun to solve these problems by developing new computational methods, experiment- ing with unique robotic systems and fabricating tiny, artificial propellers, like bacterial flagella, to propel nanocreations through their environment.

In Proceedings of the 53rd IEEE Conference on Decision and Control, Los Angeles, CA, 2014 (inproceedings)

Abstract

An approach for distributed and event-based state estimation that was proposed in previous work [1] is analyzed and extended to practical networked systems in this paper. Multiple sensor-actuator-agents observe a dynamic process, sporadically exchange their measurements over a broadcast network according to an event-based protocol, and estimate the process state from the received data. The event-based approach was shown in [1] to mimic a centralized Luenberger observer up to guaranteed bounds, under the assumption of identical estimates on all agents. This assumption, however, is unrealistic (it is violated by a single packet drop or slight numerical inaccuracy) and removed herein. By means of a simulation example, it is shown that non-identical estimates can actually destabilize the overall system. To achieve stability, the event-based communication scheme is supplemented by periodic (but infrequent) exchange of the agentsâ?? estimates and reset to their joint average. When the local estimates are used for feedback control, the stability guarantee for the estimation problem extends to the event-based control system.

We have developed a millimeter-scale magnetically driven swimming robot for untethered motion at mid to low Reynolds numbers. The robot is propelled by continuous undulatory deformation, which is enabled by the distributed magnetization profile of a flexible sheet. We demonstrate control of a prototype device and measure deformation and speed as a function of magnetic field strength and frequency. Experimental results are compared with simple magnetoelastic and fluid propulsion models. The presented mechanism provides an efficient remote actuation method at the millimeter scale that may be suitable for further scaling down in size for microrobotics applications in biotechnology and healthcare

We report on the enhanced optical properties of chiral magnetic nanohelices with critical dimensions comparable to the ferromagnetic domain size. They are shown to be ferromagnetic at room temperature, have defined chirality, and exhibit large optical activity in the visible as verified by electron microscopy, superconducting quantum interference device (SQUID) magnetometry, natural circular dichroism (NCD), and magnetic circular dichroism (MCD) measurements. The structures exhibit magneto-chiral dichroism (MChD), which directly demonstrates coupling between their structural chirality and magnetism. A chiral nickel (Ni) film consisting of an array of nanohelices similar to 100 nm in length exhibits an MChD anisotropy factor g(MChD) approximate to 10(-4) T-1 at room temperature in a saturation field of similar to 0.2 T, permitting polarization-independent control of the film's absorption properties through magnetic field modulation. This is also the first report of MChD in a material with structural chirality on the order of the wavelength of light, and therefore the Ni nanohelix array is a metamaterial with magnetochiral properties that can be tailored through a dynamic deposition process.

Miniaturized structures that can move in a controlled way in solution and integrate various functionalities are attracting considerable attention due to the potential applications in fields ranging from autonomous micromotors to roving sensors. Here we introduce a concept which allows, depending on their specific design, the controlled directional motion of objects in water, combined with electronic functionalities such as the emission of light, sensing, signal conversion, treatment and transmission. The approach is based on electric field-induced polarization, which triggers different chemical reactions at the surface of the object and thereby its propulsion. This results in a localized electric current that can power in a wireless way electronic devices in water, leading to a new class of electronic swimmers (e-swimmers).

Photoresponsive N-isopropylacrylamide ionogel microstructures are presented in this study. These ionogels are synthesised using phosphonium based room temperature ionic liquids, together with the photochromic compound benzospiropyran. The microstructures can be actuated using light irradiation, facilitating non-contact and non-invasive operation. For the first time, the characterisation of the swelling and shrinking behaviour of several photopatterned ionogel microstructures is presented and the influence of surface-area-to-volume ratio on the swelling kinetics is evaluated. It was found that the swelling and shrinking behaviour of the ionogels is strongly dependent on the nature of the ionic liquid. In particular, the {[}P-6,P-6,P-6,P-14]{[}NTf2] ionogel exhibits the greatest degree of swelling, reaching up to 180\% of its initial size, and the fastest shrinkage rate (k(sh) = 29 +/- 4 x 10(-2) s(-1)). (C) 2014 Elsevier B. V. All rights reserved.

As we move towards the miniaturization of devices to perform tasks at the nano and microscale, it has become increasingly important to develop new methods for actuation, sensing, and control. Over the past decade, bio-hybrid methods have been investigated as a promising new approach to overcome the challenges of scaling down robotic and other functional devices. These methods integrate biological cells with artificial components and therefore, can take advantage of the intrinsic actuation and sensing functionalities of biological cells. Here, the recent advancements in bio-hybrid actuation are reviewed, and the challenges associated with the design, fabrication, and control of bio-hybrid microsystems are discussed. As a case study, focus is put on the development of bacteria-driven microswimmers, which has been investigated as a targeted drug delivery carrier. Finally, a future outlook for the development of these systems is provided. The continued integration of biological and artificial components is envisioned to enable the performance of tasks at a smaller and smaller scale in the future, leading to the parallel and distributed operation of functional systems at the microscale.

Efficient manipulation requires contact to reduce uncertainty. The manipulation literature refers to this as funneling: a methodology for increasing reliability and robustness by leveraging haptic feedback and control of environmental interaction. However, there is a fundamental gap between traditional approaches to trajectory optimization and this concept of robustness by funneling: traditional trajectory optimizers do not discover force feedback strategies. From a POMDP perspective, these behaviors could be regarded as explicit observation actions planned to sufficiently reduce uncertainty thereby enabling a task. While we are sympathetic to the full POMDP view, solving full continuous-space POMDPs in high-dimensions is hard. In this paper, we propose an alternative approach in which trajectory optimization objectives are augmented with new terms that reward uncertainty reduction through contacts, explicitly promoting funneling. This augmentation shifts the responsibility of robustness toward the actual execution of the optimized trajectories. Directly tracing trajectories through configuration space would lose all robustness-dual execution achieves robustness by devising force controllers to reproduce the temporal interaction profile encoded in the dual solution of the optimization problem. This work introduces dual execution in depth and analyze its performance through robustness experiments in both simulation and on a real-world robotic platform.

Bounded rationality concerns the study of decision makers with limited information processing resources. Previously, the free energy difference functional has been suggested to model bounded rational decision making, as it provides a natural trade-off between an energy or utility function that is to be optimized and information processing costs that are measured by entropic search costs. The main question of this article is how the information-theoretic free energy model relates to simple \(\epsilon\)-optimality models of bounded rational decision making, where the decision maker is satisfied with any action in an \(\epsilon\)-neighborhood of the optimal utility. We find that the stochastic policies that optimize the free energy trade-off comply with the notion of \(\epsilon\)-optimality. Moreover, this optimality criterion even holds when the environment is adversarial. We conclude that the study of bounded rationality based on \(\epsilon\)-optimality criteria that abstract away from the particulars of the information processing constraints is compatible with the information-theoretic free energy model of bounded rationality.

Our goal is to understand the principles of Perception, Action and Learning in autonomous systems that successfully interact with complex environments and to use this understanding to design future systems